(Supported by the NH/NCRR/P-41 RR 01219 grant) Averaging techniques that are usually applied for noise reduction in repeating structures cannot be used for tomographic reconstructions of individual non-repeating structures. Noise reduction based on wavelet transformation, along with a nonlinear filtration of the transform coefficients has been reported (A. Stoschek et al, (1997) J. of Structural Biology 120, 257-265) to be better than conventional filter techniques, such as the median filter or the Wiener filter. In periodic signals, Fourier analysis is the optimum transformation to separate signal and noise. However in non-periodic signals, wavelet transformation seems to be the most efficient method. Using the wavelet method, structural elements in the image data are characterized by orientation and extension, and are transformed to a small number of large coefficients, whereas noise (being devoid of these characteristics) is transformed into a large number of small coefficients. Based on a suitable nonlinear thresholding technique, these small coefficients are set to zero to reduce the noise. The wavelet transformation is done on the reconstructed volume using a non-linear filter in the transform domain. The strength of denoising and the preservation of structural features are controlled by the size of threshold parameter. Currently, a threshold parameter of twice the median, taken from the moduli of transform coefficients, has been reported to work well, based on a few actual and tes t volumes. These results need to be checked when applied to a large number of different reconstructed volumes. The denoising software routines developed by the group of Dr. Reiner Hegerl at the Max Planck Instutute in Matrinsried, as well as WAVELAB, a set of programs written by a group at Stanford, were obtained. These programs are integrated into the MATLAB software system. Modifications were made to MATLAB for processing SPIDER image files. Work is in progress to access these routines directly from SPIDER. Suitable input parameters for the wavelet-based denoising schemes are under study.